%0 Journal Article
%T Multi-sensor optimal information fusion steady-state Kalman filterweighted by scalars for systems with colored measurement noises<br>带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器
%A SUN Shu-li
%A DENG Zi-li
%A <br>孙书利
%A 邓自立
%J 控制理论与应用
%D 2004
%I 
%X Based on the multi_sensor optimal information fusion criterion weighted by scalars in the linear minimum variance,a scalar weighting information fusion steady_state Kalman filter with a two_layer fusion structure is given for discrete linear stochastic control systems measured by multiple sensors with colored measurement noises,which is equivalent to an optimal information fusion steady_state Kalman predictor for the corresponding systems with correlated noises.The optimal information fusion steady_state predictor can be obtained only by fusing once after all local predictors reach the steady state.The solutions of steady_state prediction error cross_covariance matrices between any two subsystems can be obtained by iteration with arbitrary initial values,whose convergence is proved.Its effectiveness is shown by applying it to a radar tracking system with three sensors.
%K multi_sensor
%K scalar weighting optimal information fusion
%K steady_state Kalman filter
%K colored measurement noises
%K radar tracking system<br>多传感器
%K 标量加权最优信息融合
%K 稳态Kalman滤波器
%K 有色观测噪声
%K 雷达跟踪系统
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=F9727CB02FB54732&yid=D0E58B75BFD8E51C&vid=659D3B06EBF534A7&iid=E158A972A605785F&sid=20C9FB8C7B4A22AD&eid=50B6AC44200581A5&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=1&reference_num=8